Thursday, December 27, 2012

If you have been wondering where new blog posts are, I am taking a holiday hiatus. I'll see you in January when biweekly posting will be resumed, and the cellular scale will celebrate its first birthday!

Thursday, December 20, 2012

Sure playing video games (and therefore shooting in video games) releases dopamine, and sure if you inject dopamine into people while they shoot real guns they will like shooting guns better. BUT the key implication here, that shooting guns in video games makes you like shooting real guns demands evidence.

As a female Halo player myself, I think these Lady Spartans are awesome! (source)

Personally, I like shooter video games. I'm playing Halo 4 like the rest of the world right now and I played the heck out of Mass Effect earlier in the year. I have also shot real guns.

And guess what? shooting real guns is just not really my thing. I find it a little bit scary and not that fun or exciting. The idea of going to a shooting range and shooting guns at paper targets for an hour sounds really boring to me. Shooting skeet or something moving, like an animal, also sounds pretty boring.

I am skeptical about the idea that the dopamine released during shooting video games transfers to more enjoyment while shooting real guns. I am willing to change my mind upon seeing some data, but having seen nothing to support this direct transfer, I don't think it exists.

This post is written in response to "Addicted to the Bang: The neuroscience of the gun." by Steve Kotler and Jim Olds. (They don't actually claim that dopamine release during video game shooting directly causes addiction to real shooting, but I think that someone might get that idea from the article.)

To add these channels you have to extract the parameters from known data. This means extracting Boltzmann curves and time constant information so you can tell the channel which voltages activate it and inactivate it and how fast to open and close.

Activation (Boltzmann) curve for fast sodium channel

This step is tricky and can take a long time, but there is some software that can help. The Enguage Digitizer is one tool I could not live without.

Enguage is basically a tool that allows you to manually trace curves from published figures to get the curve data as an excel or .csv file. First you add axis points using the button at the top that has red plus signs on it. You tell the software what values each of the 3 corners of the graph are. Then you click the blue plus signs button and start to trace your graph, like so:

using Enguage digitizer to extract channel data

Then you export the data as whichever type of file you want. Pretty nice!
I like to have the data this way because then I can overlay this figure trace with any other trace I want and can manually fit an equation to it.

Channels are a hugely important part of a computational model. A recent paper from Eve Marder's lab shows that even with a very simple morphological model (just a soma), interesting electrical characteristics can be seen simply by manipulating the channels.

Kispersky et al., (2012) introduce an interesting paradox. They show that when you increase the sodium channel conductance you see more action potentials with low current injections (like 200pA). This is expected because the sodium channel is what causes the upswing of the action potential and more sodium is thought to mean more excitability. However, the authors find that when a high current injection is given (like 10nA), the increased sodium channel conductance actually decreases the firing rate. This is counter-intuitive because it goes against the more sodium=more excitability rule.

This is a pretty cool finding published in the Journal of Neuroscience using only a simple one-compartment model. The finding is based entirely on channel manipulation, and demonstrates how important these intrinsic channels are to any computational model.

Thursday, December 13, 2012

And now, let me answer your Seriously Deep Questions. All questions answered can be found in the LMAYQ index. And as always these are real true search terms that the all-knowing Internet directed to The Cellular Scale. Let's begin.

It is my personal opinion that thoughts do not actually look like anything. I've dissected many a brain and haven't ever seen one. However, let's suppose thoughts look like something, what would they look like?

One possibility is that the thought looks like what you are thinking about. A pretty ancient idea is that there are actually two of every object, one that is external (the actual object), and one that is internal which is our representation of that object. This can be taken quite literally in which case if you are looking at or thinking about a tree, your thought will look like a tree, but if you are thinking about a dog, your thought will look like a dog. This strikes me as unlikely.

So another way to look at it is what does the brain look like when it is having a thought? In this case there is some support for the 'thought looks like what you are thinking' hypothesis, but it is very limited.

Above is a famous example of how a visual stimulus can be reflected in the brain in a very literal way. In this case a monkey looks at a grid and the activation pattern in the brain looks like a grid. But these days 'thoughts' usually look like this:

And there is no obvious or literal relationship between the shape of the fMRI image and the thought that is thunk.

2. "Why Neuroscience?"

Because neuroscience is our best chance at answering important questions like 'what do thoughts look like?' and 'How do we know what we know?'

3. "Do neurons tell you how to move or do they fire in response?"

Another excellent and deep question. The answer is (of course) that they do both.

People used to think of the brain as a black box, where sensory input comes in (like through your eyes) and gets 'processed' by the brain and a motor output comes out (like through your hands).

All of these steps, the sensory input, the motor output, and the processing in between take neurons.
But of course there is the Venus flytrap which doesn't have 'neurons' per se, but does receive sensory input and generate motor output.

But the processing part of this process, the black box, is really complicated. There really is an unanswered question there about whether neurons are responding to something or telling something. When studies find that mirror neurons fire 'in response to' seeing actions performed, or that some amygdala neurons fire in response to pictures of animals, the question is always why are these neurons firing? Are the neurons telling another part of the brain 'this is an animal'? or are the neurons responding to that information?

The authors used two learning tasks to investigate how spines grow during learning. In the "reaching" task, mice had to reach their paw into a slit and grab a seed. In the "capellini handling task" the mouse is given a 2.5 cm length of (I am not making this up) angel hair pasta and learns how to handle it for eating. learning is measured by how fast the mouse eats the pasta.

The interesting difference between learning a specific task rather than just playing is that the spines grow in distinct clusters when the mice are taught a learning task. C shows the total spine growth, while D shows the proportion of clustered spines to total spines. Reach only means the mice were only taught the reaching task, and cross-training means they were taught both the reaching task and the pasta handling task.

The authors explain two possible functions for these spine clusters:

"Positioning multiple synapses between a pair of neurons in close
proximity allows nonlinear summation of synaptic strength, and
potentially increases the dynamic range of synaptic transmission well
beyond what can be achieved by random positioning of the same number of
synapses."

Meaning spines that are clustered and receive inputs from the same neuron have more power to influence the cell than spines further apart.

"Alternatively, clustered new spines may synapse with distinct (but presumably functionally related) presynaptic partners.
In this case, they could potentially integrate inputs from different
neurons nonlinearly and increase the circuit’s computational power. "

Meaning that maybe the spines don't receive input from the same neuron, but are clustered so they can integrate signals across neurons more powerfully.

And of course...

"Distinguishing between these two possibilities would probably require circuit reconstruction by electron microscopy following in vivo imaging to reveal the identities of presynaptic partners of newly formed spines."